Hydromet 2021 – Advances in Hydro-meteorological Studies

The increasing frequency and severity of climate-related disasters necessitate robust prediction systems to mitigate risks and enhance adaptive capacity. The objective of this study is to develop and evaluate data-driven methodologies for predicting climate risk at regional and local levels. Utilizing a combination of historical climate data, advanced statistical techniques, and machine learning algorithms, this research establishes a framework for climate risk assessment. The methodology includes data collection from multiple sources, preprocessing for quality assurance, and implementation of predictive models to analyze risk factors associated with climate variability. Results indicate a significant improvement in prediction accuracy compared to traditional methods, with the machine learning models outperforming basic statistical approaches. Additionally, the framework demonstrates applicability across diverse geographic regions, highlighting its versatility in addressing climatic challenges globally. Overall, the findings underscore the potential of data-driven techniques in enhancing climate risk prediction and inform decision-making processes for effective climate change adaptation.